SmartBear CRO Dave Phillips says the rapid adoption of AI-assisted coding is creating new software quality, testing, and API governance challenges for enterprises — and new services opportunities for channel partners.
As development teams ship code faster with AI tools, many organizations are struggling to modernize testing and governance at the same pace. Phillips told Channel Insider that partners can help close that gap by integrating automated testing, API governance, and software quality controls into customer development workflows.
The newly appointed SmartBear revenue chief also outlined the company’s partner strategy, including deeper alignment with AWS and Atlassian and a broader push to help solution providers build services around AI-driven software development.
AI-assisted development widens the software quality gap
AI is dramatically accelerating software development. How are you seeing this change the conversations your partners are having with enterprise customers around testing, API management, and software quality?
AI is transforming partner conversations across the board. As AI accelerates development, it’s also amplifying testing bottlenecks, and partners are actively looking for solutions to help their customers manage that change and maintain application integrity, which is continuous, measurable assurance that software works as intended.
SmartBear is delivering the tools partners need to build quality into every stage of the pipeline, from agentic testing with BearQ to advanced automation with Reflect. AI is embedded at the core of our solutions, not bolted on.
Many organizations are adopting AI coding assistants faster than they are modernizing their testing practices. What risks are you seeing emerge, and where do partners have the biggest opportunity to help customers close those gaps?
The speed of AI-assisted coding is outpacing most organizations’ testing and governance practices.
We’re already seeing the results: over 90% have adopted AI coding tools, but nearly as many still test manually, and 60% report quality has already slipped because development is outrunning validation, according to a recent SmartBear study, Closing the AI Software Quality Gap.
That’s the risk. The opportunity for partners is closing that gap directly, pairing the right testing and API governance tools with the integration and process work to make validation automatic instead of another manual step customers can’t keep up with.
SmartBear builds its partner strategy around AWS and Atlassian
As SmartBear’s new CRO, what are your top priorities for expanding and strengthening the partner ecosystem over the next 12 to 18 months?
My top priority is deepening our alignment with the platforms partners and customers already build on, rather than asking them to adopt something new.
Two recent moves show what that looks like. With Atlassian, we’re building Rovo skills and agents so application integrity capabilities show up directly inside tools teams already use daily.
With AWS, we just achieved AWS AI Software Competency status in the Agentic AI category, which validates that products like QMetry, Swagger, Reflect, and BugSnag meet AWS’s bar for security, reliability, and operational excellence, building on our existing AWS DevOps ISV Competency and Strategic Collaboration Agreement.
For partners, that translates directly into a stronger go-to-market position: a validated, trusted path to sell AI-powered quality solutions into their AWS and Atlassian customer bases, backed by rigorous third-party technical validation rather than our word alone.
Over the next 12 to 18 months, my focus is expanding these kinds of platform alignments so partners can lead with proven, integrated solutions instead of point products.
What kinds of investments or enablement is SmartBear making to help partners build services around AI-driven software quality, testing, and API governance?
We’re focusing on deep product training so partners can quickly activate capabilities and deliver customer value, giving them the ability to build assessments, and helping grow their services practice around software quality, testing, and API solutions.
AI changes how enterprise buyers evaluate software
Enterprise customers are under pressure to deliver software faster while maintaining security and compliance. How is AI changing software purchasing decisions, and what should channel partners understand about evolving buyer priorities?
Enterprise buyers are under more pressure than ever to ship faster, and AI is accelerating that pressure, not relieving it. The shift isn’t just in how software gets built; it’s in how it gets bought. Buyers are moving away from evaluating features and toward asking a simpler question: does this solve my problem, and can I prove the value?
A few things are driving this. Speed creates quality debt. The cost model is shifting, from seat-based to consumption and outcome-based pricing. Security and compliance are now board-level topics, and governance isn’t a checkbox, it’s required across the executive team.
For channel partners, the implication is clear: the winning motion is leading with business outcomes, time to release, defect rates, compliance posture, and working back to the product.
Looking ahead, where do you see the biggest growth opportunities for solution providers as AI becomes a standard part of the software development lifecycle?
The biggest macro trend driving opportunity is this: AI is compressing time and effort across phases of the Software Development Life Cycle (SDLC) that have historically been neglected, including requirements specification, peer code review, compliance testing, and incident response.
Teams building faster with AI are shipping more code than their QA processes can handle. That gap is the opportunity.
Phillips was one of several channel leaders taking on new roles in July. Read more about the latest executive appointments at Veeam, QuSecure, Flexera, and other channel organizations in Channel Insider’s July 2026 Channel Leadership recap.